Data Frame Explorer Documentation¶

Version: 1.0 (Jupytext, time measurements, logger)

Table of Content¶

  • Notebook Description
  • General Settings
    • Paths
    • Notebook Functionality and Appearance
    • External Libraries
    • Internal Code
    • Constants
  • Analysis
    • Data Frame
    • Exploration
      • Info about Data Frame
      • Attribute Types
      • Memory Usage
      • NaN Statistics
      • Attribute Statistics
    • Data Frames Comparison
  • Final Timestamp

Notebook Description¶

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Please put your comments about the notebook functionality here.

GENERAL SETTINGS¶

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General settings for the notebook (paths, python libraries, own code, notebook constants).

NOTE: All imports and constants for the notebook settings shoud be here. Nothing should be imported in the analysis section.

Paths¶

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Adding paths that are necessary to import code from within the repository.

In [1]:
import sys
import os
sys.path+=[os.path.join(os.getcwd(), ".."), os.path.join(os.getcwd(), "../..")] # one and two up

Notebook Functionality and Appearance¶

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Necessary libraries for notebook functionality:

  • A button for hiding/showing the code. By default it is deactivated and can be activated by setting CREATE_BUTTON constant to True.

    NOTE: This way, using the function, the button works only in active notebook. If the functionality needs to be preserved in html export, then the code has to be incluced directly into notebook.

  • Set notebook width to 100%.
  • Notebook data frame setting for better visibility.
  • Initial timestamp setting and logging the start of the execution.
In [2]:
from src.utils.notebook_support_functions import create_button, get_notebook_name
from src.utils.logger import Logger
from src.utils.envs import Envs
from src.utils.config import Config
from pandas import options
from IPython.display import display, HTML

Constants for overall behaviour.

In [3]:
LOGGER_CONFIG_NAME = "logger_file_console" # default
PYTHON_CONFIG_NAME = "python_local" # default
CREATE_BUTTON = False
ADDAPT_WIDTH = False
NOTEBOOK_NAME = get_notebook_name()
In [4]:
options.display.max_rows = 500
options.display.max_columns = 500
envs = Envs()
envs.set_logger(LOGGER_CONFIG_NAME)
envs.set_config(PYTHON_CONFIG_NAME)
Logger().start_timer(f"NOTEBOOK; Notebook name: {NOTEBOOK_NAME}")
if CREATE_BUTTON:
    create_button()
if ADDAPT_WIDTH:
    display(HTML("<style>.container { width:100% !important; }</style>")) # notebook width

A: ../../configurations\logger_file_console.conf
2023-03-21 16:18:42,821 - git.util - DEBUG - Failed checking if running in CYGWIN due to: FileNotFoundError(2, 'The system cannot find the file specified', None, 2, None)
2023-03-21 16:18:42,824 - file_console - DEBUG - Logger was created on WS-3000 in branche 005_cumulative_repository_update.
2023-03-21 16:18:42,824 - file_console - DEBUG - Process: NOTEBOOK; Notebook name: data_frame_explorer_documentation.py; Timer started;

External Libraries¶

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In [5]:
from importlib import reload
from pandas import DataFrame
from numpy.random import choice, randn, seed

Internal Code¶

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Code, libraries, classes, functions from within the repository.

In [6]:
import src.data.df_explorer as DFE

Constants¶

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Constants for the notebook.

NOTE: Please use all letters upper.

General Constants¶

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In [7]:
# from src.global_constants import *  # Remember to import only the constants in use
N_ROWS_TO_DISPLAY = 2
FIGURE_SIZE_SETTING = {"autosize": False, "width": 2200, "height": 750}

Constants for Setting Automatic Run¶

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In [ ]:

Notebook Specific Constants¶

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In [ ]:

ANALYSIS¶

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In [8]:
config = Config().get_data()

2023-03-21 16:18:42,962 - file_console - DEBUG - Python config was created from python_local.conf file.

Data Frame¶

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In [9]:
df = DataFrame()
n = 100
seed(876)
df["sex"] = choice(["male", "female"], n)
df["number"] = randn(n)

df.head()
Out[9]:
sex number
0 male -0.087944
1 female -1.078677
2 female -1.269129
3 female -0.616576
4 female -0.390710

Exploration¶

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In [10]:
reload(DFE)
df_explorer = DFE.DFExplorer()

Info about Data Frame¶

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In [11]:
df_explorer.print_info_about_data_frame(df=df)

DataFrame type: <class 'pandas.core.frame.DataFrame'>
DataFrame shape: (100, 2)
DataFrame dtypes: {'sex': 'object', 'number': 'float64'}


DataFrame head:
      sex    number
0    male -0.087944
1  female -1.078677
2  female -1.269129
3  female -0.616576
4  female -0.390710


DataFrame description:
           number
count  100.000000
mean    -0.109604
std      1.137798
min     -3.088058
25%     -0.780472
50%     -0.143400
75%      0.600610
max      3.264726

Attribute Types¶

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In [12]:
df_explorer.get_df_types(df=df)
Out[12]:
{'sex': 'object', 'number': 'float64'}

Memory Usage¶

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In [13]:
df_explorer.get_memory_usage(df=df, attr_name="number", list_dtypes=["float64", "float32", "float16"])

Memory usage for attribute: number
  Attribute Name: number
  Measured dtype: float64
  Memory Usage: 928


  Attribute Name: number
  Measured dtype: float32
  Memory Usage: 528


  Attribute Name: number
  Measured dtype: float16
  Memory Usage: 328

NaN Statistics¶

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In [14]:
df_explorer.get_nan_stats(df=df, fraction=True)

DataFrame shape: (100, 2)
Total number of NaN values: 0
NaN values per Attribute: 

Attribute      NaN values    Fraction
-----------  ------------  ----------
sex                     0           0
number                  0           0
TOTAL                   0           0
In [15]:
df_explorer.get_nan_stats(df=df, fraction=False)

DataFrame shape: (100, 2)
Total number of NaN values: 0
NaN values per Attribute: 

sex       0
number    0
dtype: int64

Attribute Statistics¶

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In [16]:
df_explorer.print_attr_stats(df=df)

Attribute Name: sex
 Attribute type: object
 Number of Null values: 0
 Number of unique values is:2
 Percentage of unique values is: 0.02


Summation of unique values per ID:
male      55
female    45
Name: sex, dtype: int64




#############################################
Attribute Name: number
 Attribute type: float64
 Number of Null values: 0
 Number of unique values is:100
 Percentage of unique values is: 1.0




#############################################

Data Frames Comparison¶

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In [17]:
data_1 = [
    [1., 2., 3.],
    [3., 2., 1.],
    [4., 5., 2.]
]
data_2 = [
    [1., 2., 3.],
    [3., 10., 1.],
    [4., 5., 2.]
]
attr_names = ["NUMBER_1", "NUMBER_2", "NUMBER_3"]
df_1 = DataFrame(data_1, columns=attr_names)
df_2 = DataFrame(data_2, columns=attr_names)
In [18]:
# identical data frames
df_explorer.compare_attributes_in_data_frames(df_1, df_1, attr_names)

Are DFs equal in pandas? True
Checking Overall Sums for DFs
 - Sum of First List is: 23.0
 - Sum of Second List is: 23.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0 
Checking for Attribute: NUMBER_1
 - Sum of First List is: 8.0
 - Sum of Second List is: 8.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0 
Checking for Attribute: NUMBER_2
 - Sum of First List is: 9.0
 - Sum of Second List is: 9.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0 
Checking for Attribute: NUMBER_3
 - Sum of First List is: 6.0
 - Sum of Second List is: 6.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0
In [19]:
# not identical data frames
df_explorer.compare_attributes_in_data_frames(df_1, df_2, attr_names)

Are DFs equal in pandas? False
Checking Overall Sums for DFs
 - Sum of First List is: 23.0
 - Sum of Second List is: 31.0
 - Subtraction of Sums of Lists is: -8.0
 - Percentage of Difference (1-2)/1 is: -0.34782608695652173 
 - Percentage of Difference (1-2)/2 is: -0.25806451612903225 
Checking for Attribute: NUMBER_1
 - Sum of First List is: 8.0
 - Sum of Second List is: 8.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0 
Checking for Attribute: NUMBER_2
 - Sum of First List is: 9.0
 - Sum of Second List is: 17.0
 - Subtraction of Sums of Lists is: -8.0
 - Percentage of Difference (1-2)/1 is: -0.8888888888888888 
 - Percentage of Difference (1-2)/2 is: -0.47058823529411764 
Checking for Attribute: NUMBER_3
 - Sum of First List is: 6.0
 - Sum of Second List is: 6.0
 - Subtraction of Sums of Lists is: 0.0
 - Percentage of Difference (1-2)/1 is: 0.0 
 - Percentage of Difference (1-2)/2 is: 0.0

Final Timestamp¶

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In [20]:
Logger().end_timer()

2023-03-21 16:18:43,415 - file_console - DEBUG - Process: NOTEBOOK; Notebook name: data_frame_explorer_documentation.py; Timer ended; Process Duration [s]: 0.59; Process Duration [m]: 0.01